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Female Body and Sexual Politics in Margaret Atwood's Selected Novels

2015· article· en· W1467857179 on OpenAlexaboutno aff
Elaheh Soofastaei, Sayyed Ali Mirenayat

Bibliographic record

VenueInternational Letters of Social and Humanistic Sciences · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicUtopian, Dystopian, and Speculative Fiction
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsIdeologyHuman sexualitySilenceFeminismGender studiesSymbol (formal)Human rightsSociologyDominance (genetics)FeelingLiteratureLawAestheticsArtPsychologyPhilosophyPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Margaret Atwood is the most prominent Canadian writer. Her feminist ideology is clearly obvious in her novels. She overtly illustrates her feminism view in human rights equality and freedom of choice. Atwood's works are consisted of the fundamental freedom and human rights. In general, her fictions truly portray the women's rights that are equal to men's rights. Social constructions of gender are attacked by Atwood's novels. Her stories represent the silence and sexual discrimination in female characters. She is not only looking for annihilating of the gender system i.e. women's subjugation, but look at men and women at the same level in society. Female bodies in Atwood's point of view have been captured in patriarchal societies. Female protagonists in the selected novels explain noticeable symbols of bodily nervousness. Female characters are mostly used as objects in Atwood's stories. Women are considered as a tool or toy, as if they have no feelings, opinions or rights of their own. Body in female characters plays an important role and it is symbol of sexuality. Female body in Atwood's selected stories is under the cruel dominance by male and that is what she always tries to portray under the sexual politics. This paper aims to illustrate sexual politics though female body in Atwood's selected works.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.316

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.076
GPT teacher head0.289
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2015
Admission routes1
Has abstractyes

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